A pithy description of NNST and non-parametric neural style transfer algorithm proposed by Chen and Schmidt[1]} much earlier in 2016 would reveal little difference between the two. Both methods explicitly construct a tensor of `target features' by replacing vectors of VGG-derived content features with vectors of VGG-derived style features, then optimize the pixels of the output image to produce the 'target features' (or use a learned decoder). Yet, there is a dramatic difference between the visual quality of the algorithms' outputs. As is often the case, the devil is in the details, and this section explores the important design decisions that can boost a style transfer algorithm's visual quality.
